Home⇛LPU-Laguna Journal of Engineering and Computer Studies⇛vol. 5 no. 3 (2023)

Face Recognition using Artificial Neural Network (Ann) as Automated Attendance for Bachelor of Science in Electrical Engineering in Lpu-Laguna

Richpaul Q. Bigal | Ma. Elena Valentina P. Calabia | Lyka Joyce T. Lanaria

Discipline: electrical and electronic engineering

 

Abstract:

This study developed a face recognition-based automated attendance system using an Artificial Neural Network (ANN) and Python for Bachelor of Science in Electrical Engineering students at Lyceum of the Philippines University-Laguna. The system was designed to improve the efficiency, accuracy, and reliability of attendance monitoring while minimizing manual intervention. Facial images of students were captured through a camera and processed using image enhancement, histogram normalization, and face detection techniques. The ANN model was trained using facial datasets with varying lighting conditions and orientations to improve recognition performance. Attendance records were automatically stored in a database, and SMS notifications were sent to parents through a GSM module to provide real-time attendance updates. The system integrated Python programming, OpenCV libraries, Arduino Nano, and GSM technology to achieve automated identification and communication functions. Experimental results showed that the proposed system achieved a 95% recognition rate for frontal face orientations, while recognition accuracy decreased as the face angle increased. The findings indicate that the developed system provides a cost-effective, time-saving, and user- friendly alternative to conventional attendance monitoring methods. Furthermore, the study demonstrates the potential application of ANN- based face recognition systems in educational institutions and other environments requiring secure identification and access monitoring.



References:

  1. Z. Pazoki and F. Farokhi, "Effective Feature Selection for Face Recognition Based on Correspondence Analysis and Trained Artificial Neural Network," IEEE, pp. 80-84, Dec. 2010, doi: 10.1109/sitis.2010.23.
  2. J. Salyut and C. Kurnaz, "Profile Face Recognition using Local Binary Patterns with Artificial Neural Network," IEEE, vol. 3021, pp. 1-4, Sep. 2018, doi: 10.1109/idap.2018.8620840.
  3. N. Deotale, S. L. Vaikole, and S. D. Sawarkar, "Face recognition using artificial neural networks," IEEE, vol. 60, pp. 446-450, Feb. 2010, doi: 10.1109/iccae.2010.5451595.
  4. K.-M. Lim, Y.-C. Sim, and K.-W. Oh, "A face recognition system using fuzzy logic and artificial neural network," IEEE Conference Publication | IEEE Xplore, 1992. https://ieeexplore.ieee.org/document/258801
  5. L. Masupha, T. Zuva, S. Ngwira, and O. Esan, "Face recognition techniques, their advantages, disadvantages and performance evaluation," IEEE Conference Publication | IEEE Xplore, Dec. 01, 2015. https://ieeexplore.ieee.org/document/7374154
  6. C. Tanchotsrinon, S. Phimoltares, and S. Maneeroj, "Facial expression recognition using graph-based features and artificial neural networks," IEEE, pp. 331-334, May 2011, doi: 10.1109/ist.2011.5962229.
  7. J. Wang and J. Yin, "Face recognition by global optimal discriminant features and ensemble artificial neural networks," IEEE Conference Publication | IEEE Xplore, Dec. 01, 2009. https://ieeexplore.ieee.org/document/5374762
  8. Y. Ren, K. M. Iftekharuddin, and W. E. White, "Large-scale pose-invariant face recognition using cellular simultaneous recurrent network," Applied Optics, vol. 49, no. 10, p. B92, Mar. 2010, doi: 10.1364/ao.49.000b92.
  9. Z. Wu, M. Peng, and T. Chen, "Thermal face recognition using convolutional neural network," IEEE Conference Publication | IEEE Xplore, Jun. 01, 2016. https://ieeexplore.ieee.org/document/7528489
  10. S. A. Nazeer, N. Omar, K. F. Jumari, and M. Khalid, "Face detecting using artificial neural network approach," IEEE Conference Publication | IEEE Xplore, Mar. 01, 2007. https://ieeexplore.ieee.org/document/4148693
  11. A. S. Khan, I. Shafi, M. Anas, B. M. Yousuf, M. J. Abbas, and A. Noor, "Facial Expression Recognition using Discrete Cosine Transform & Artificial Neural Network," IEEE Conference Publication | IEEE Xplore, Nov. 01, 2019. https://ieeexplore.ieee.org/document/9022749
  12. H. N. Somehsaraei, M. Hölle, H. Hönen, and M. Assadi, "A novel approach based on artificial neural network for calibration of multi-hole pressure probes," Flow Measurement and Instrumentation, vol. 73, p. 101739, Apr. 2020, doi: 10.1016/j.flowmeasinst.2020.101739.
  13. N. Alsrehin and M. A. Al-Taamneh, "Face Recognition Techniques using Statistical and Artificial Neural Network: A Comparative Study," IEEE Conference Publication | IEEE Xplore, Mar. 01, 2020. https://ieeexplore.ieee.org/document/9092128
  14. K. Yamamori, S. Horiguchi, J. H. Kim, S. K. Park, and B. H. Ham, "The efficient design of fault-tolerant artificial neural networks," IEEE Conference Publication | IEEE Xplore, 1995. https://ieeexplore.ieee.org/document/487381
  15. S. Biswas, K. W. Bowyer, and P. J. Flynn, "A study of face recognition of identical twins by humans," IEEE, pp. 1-6, Nov. 2011, doi: 10.1109/wifs.2011.6123126.
  16. P. J. Phillips et al., "Distinguishing identical twins by face recognition," IEEE Conference Publication | IEEE Xplore, Mar. 01, 2011. https://ieeexplore.ieee.org/abstract/document/5771395
  17. K. I. Kim, K. Jung, and H. J. Kim, "Face recognition using kernel principal component analysis," IEEE Journals & Magazine | IEEE Xplore, Feb. 01, 2002. https://ieeexplore.ieee.org/document/991133
  18. R. Kaur and E. Himanshi, "Face recognition using Principal Component Analysis," IEEE Conference Publication | IEEE Xplore, Jun. 01, 2015. https://ieeexplore.ieee.org/abstract/document/7154774
  19. K. Chung, S. Kee, and S. Kim, "Face recognition using principal component analysis of Gabor filter responses," IEEE Conference Publication | IEEE Xplore, 1999. https://ieeexplore.ieee.org/document/799223
  20. K. K. Shinde, S. S. Tharewal, K. S. Suryawanshi and C. N. Kayte, "Python Based Face Recognition for Person Identification Using PCA and 2DPCA Techniques," 2020 International Conference on Smart Innovations in Design, Environment, Management, Planning and Computing (ICSIDEMPC), Aurangabad, India, 2020, pp. 171-175, doi: 10.1109/ICSIDEMPC49020.2020.9299649
  21. C. A. Trianti, B. Kristianto and Hendry, "Integration of Flask and Python on The Face Recognition Based Attendance System," 2021 2nd International Conference on Innovative and Creative Information Technology (ICITech), Salatiga, Indonesia, 2021, pp. 164-168, doi: 10.1109/ICITech50181.2021.9590122.
  22. V. Karthik, V. Nandalal, B. M. Shankar, V. Surya, R. Vetrivel and R. Zameer, "Self distancing tracker to prevent COVID - 19 by facial recognition using OpenCV," 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 2021, pp. 1399-1406, doi: 10.1109/ICECA52323.2021.9675997.
  23. J. Vadlapati, S. Senthil Velan and E. Varghese, "Facial Recognition using the OpenCV Libraries of Python for the Pictures of Human Faces Wearing Face Masks during the COVID-19 Pandemic," 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kharagpur, India, 2021, pp. 1-5, doi: 10.1109/ICCCNT51525.2021.9579712.
  24. Ravichandran and T. Adilakshmamma, "An insight of anomaly detection using machine learning Algorithm in healthcare digitalization," in Lecture notes in networks and systems, 2022, pp. 143-152. doi: 10.1007/978-981-19-5221-0_15.
  25. Y. Miyatani, S. Barua and A. Veeraraghavan, "Direct face detection and video reconstruction from event cameras," 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA, 2016, pp. 1-9, doi: 10.1109/WACV.2016.7477561.

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